A novel method of designing the linear phase typefour FIR filter based on the triangle basis neural network with a variable learning rate is presented. According to the relation of the amplitudefrequency characteristics of the linear phase typefour FIR filter and the triangle basis neural network, a triangle basis neural network model with a variable learning rate is built. In the training process of the triangle basis neural network, the value of learning rate is automatically adjusted using the variable learning rate algorithm. This strategy solves the uncertainty that  the learning rate usually is ensured according to the experiences or trial and error methods. The proposed algorithm enhances the learning efficiency and the convergence rate of the neural network. By training the neural network weight, the model makes the squared sum of amplitude frequency response error between the designed FIR filter and the ideal filter the least in the whole pass band and the cut band. The highpass filter and bandpass filter are designed using the model in this paper. The simulation results show its availability and good performance in the design of the FIR filter.